






'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class   lowerCAmelCase__ ( _lowerCAmelCase  ):
			A     				= None
			A     				= None
			A     				= None
			A     				= None






class   lowerCAmelCase__ ( _lowerCAmelCase  ):

			def __init__(					self					:		Any     ,		UpperCamelCase_					:		Any=1     ,		UpperCamelCase_					:		Optional[int]=0     ,		UpperCamelCase_					:		Union[str, Any]=2     ,		UpperCamelCase_					:		List[str]=512     ,		UpperCamelCase_					:		Optional[int]="cls"     ,		UpperCamelCase_					:		Union[str, Any]=False     ,		UpperCamelCase_					:		List[str]=True     ,		**UpperCamelCase_					:		str     ,		) ->  Dict:







				"""simple docstring"""






				super().__init__(pad_token_id=UpperCamelCase_     ,		bos_token_id=UpperCamelCase_     ,		eos_token_id=UpperCamelCase_     ,		**UpperCamelCase_	)
				lowerCamelCase_					:  Optional[Any]						=    project_dim
				lowerCamelCase_					:  List[Any]						=    pooler_fn
				lowerCamelCase_					:  int						=    learn_encoder
				lowerCamelCase_					:  Union[str, Any]						=    use_attention_mask






class   lowerCAmelCase__ ( _lowerCAmelCase  ):
			A     				= [R'''pooler''', R'''logit_scale''']
			A     				= [R'''position_ids''', R'''predictions.decoder.bias''']
			A     				= '''roberta'''
			A     				= RobertaSeriesConfig

			def __init__(					self					:		Dict     ,		UpperCamelCase_					:		Dict	) ->  int:







				"""simple docstring"""






				super().__init__(UpperCamelCase_	)
				lowerCamelCase_					:  Optional[Any]						=    XLMRobertaModel(UpperCamelCase_	)
				lowerCamelCase_					:  Union[str, Any]						=    nn.Linear(config.hidden_size     ,		config.project_dim	)
				lowerCamelCase_					:  Any						=    getattr(UpperCamelCase_     ,		'''has_pre_transformation'''     ,		UpperCamelCase_	)
				if self.has_pre_transformation:
					lowerCamelCase_					:  List[Any]						=    nn.Linear(config.hidden_size     ,		config.project_dim	)
					lowerCamelCase_					:  Tuple						=    nn.LayerNorm(config.hidden_size     ,		eps=config.layer_norm_eps	)
				self.post_init()





			def 						__UpperCamelCase				(					self					:		Any     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[torch.Tensor] = None     ,		UpperCamelCase_					:		Optional[bool] = None     ,		UpperCamelCase_					:		Optional[bool] = None     ,		UpperCamelCase_					:		Optional[bool] = None     ,		) ->  int:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    return_dict if return_dict is not None else self.config.use_return_dict

				lowerCamelCase_					:  List[Any]						=    self.base_model(
				    input_ids=UpperCamelCase_     ,		attention_mask=UpperCamelCase_     ,		token_type_ids=UpperCamelCase_     ,		position_ids=UpperCamelCase_     ,		head_mask=UpperCamelCase_     ,		inputs_embeds=UpperCamelCase_     ,		encoder_hidden_states=UpperCamelCase_     ,		encoder_attention_mask=UpperCamelCase_     ,		output_attentions=UpperCamelCase_     ,		output_hidden_states=True if self.has_pre_transformation else output_hidden_states     ,		return_dict=UpperCamelCase_     ,		)

				if self.has_pre_transformation:
					lowerCamelCase_					:  List[str]						=    outputs['''hidden_states'''][-2]
					lowerCamelCase_					:  Dict						=    self.pre_LN(UpperCamelCase_	)
					lowerCamelCase_					:  Tuple						=    self.transformation_pre(UpperCamelCase_	)

					return TransformationModelOutput(
					    projection_state=UpperCamelCase_     ,		last_hidden_state=outputs.last_hidden_state     ,		hidden_states=outputs.hidden_states     ,		attentions=outputs.attentions     ,		)
				else:
					lowerCamelCase_					:  Any						=    self.transformation(outputs.last_hidden_state	)
					return TransformationModelOutput(
					    projection_state=UpperCamelCase_     ,		last_hidden_state=outputs.last_hidden_state     ,		hidden_states=outputs.hidden_states     ,		attentions=outputs.attentions     ,		)












'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


